Model for Re-ranking Agent on Hybrid Search Engine for E-learning

The Web provides an enormous amount of learning tutorials. Searching standard content is not easy for common user of traditional search engine. The user only focuses on the top results from the enormous quantity of the arrived results. So the Re-ranking problem turns into the significant responsibility for the search systems. This paper proposes a novel model of Re-ranking Agent on Hybrid search engine (Meta-search engine and Topical search engine) for helping learners searching online Learning tutorials efficiently and in the effective way. With the aim of providing users with relevant tutorials we have proposed model classified by subject topic, prepared by professional persons and preferred by learners.